gerdm/prml resource

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

2626 stars · 544 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A collection of Jupyter notebooks implementing algorithms and reproducing figures from Christopher Bishop's book 'Pattern Recognition and Machine Learning'. It serves as a study companion covering topics from Bayesian inference to graphical models and sampling methods.

Use cases

• study machine learning algorithms from the PRML book with runnable code
• learn Bayesian linear regression and Gaussian processes through notebooks
• understand variational inference and MCMC sampling with worked examples
• reproduce figures from Bishop's Pattern Recognition and Machine Learning book
• prepare for machine learning coursework or interviews on classical ML topics
• explore implementations of SVMs, GMMs, k-means, and neural networks

When to choose

• you are reading the PRML book and want code alongside each chapter
• you prefer learning classical Bayesian machine learning through interactive notebooks
• you want reference implementations of algorithms like EM, Gibbs sampling, and Gaussian processes

When to avoid

• you need a production-ready machine learning library
• you want modern deep learning frameworks or GPU-accelerated training
• you need maintained, actively developed software with support

Facets

learning-resource · maturity maintenance

machine-learning data-science math machine-learning tutorials education python jupyter-notebooks bayesian-statistics pattern-recognition bishop-prml educational algorithms

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